Anadi Chaman

@article{khorashadizadeh2022funknn,
  title={FunkNN: Neural Interpolation for Functional Generation},
  author={Khorashadizadeh, AmirEhsan and Chaman, Anadi and Debarnot, Valentin and Dokmani{\'c}, Ivan},
  journal={ICLR},
  year={2023},
  projectpage = {https://sada.dmi.unibas.ch/en/research/implicit-neural-representation},
  volume={abs/2212.14042},
  eprint={2212.14042},
  archivePrefix={arXiv},
  url={https://openreview.net/forum?id=BT4N_v7CLrk}
}

@article{Liu2022learning,
  title={Learning multiscale convolutional dictionaries for image reconstruction},
  author={Liu, Tianlin and Chaman, Anadi and Belius, David and Dokmani{\'c}, Ivan},
  journal={IEEE Transactions on Computational Imaging},
  volume={8},
  pages={425--437},
  year={2022},
  publisher={IEEE},
  url = {https://ieeexplore.ieee.org/iel7/6745852/9679468/09775596.pdf}
}

@inproceedings{Chaman_2021_CVPR,
    author    = {Chaman, Anadi and Dokmani{\'c}, Ivan},
    title     = {Truly Shift-Invariant Convolutional Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    url       = {https://openaccess.thecvf.com/content/CVPR2021/papers/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.pdf},
    pages     = {3773-3783}
}

@inproceedings{Chaman_2021_equivariant,
  title={Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling},
  author={Chaman, Anadi and Dokmani{\'c}, Ivan},
  booktitle={55th Asilomar Conference on Signals, Systems, and Computers},
  year={2021},
  url = {https://ieeexplore.ieee.org/abstract/document/9723377/}
}

@inproceedings{chaman2019multipath,
  title={Multipath-enabled private audio with noise},
  author={Chaman, Anadi and Liu, Yu-Jeh and Casebeer, Jonah and Dokmani{\'c}, Ivan},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={685--689},
  year={2019},
  organization={IEEE},
  url = {https://ieeexplore.ieee.org/abstract/document/8683045/},
  

}

@inproceedings{ghostbuster2018mobicom,
author = {Chaman, Anadi and Wang, Jiaming and Sun, Jiachen and Hassanieh, Haitham and Roy Choudhury, Romit},
title = {Ghostbuster: Detecting the Presence of Hidden Eavesdroppers},
year = {2018},
isbn = {9781450359030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3241539.3241580},
doi = {10.1145/3241539.3241580},
abstract = {This paper explores the possibility of detecting the hidden presence of wireless eavesdroppers. Such eavesdroppers employ passive receivers that only listen and never transmit any signals making them very hard to detect. In this paper, we show that even passive receivers leak RF signals on the wireless medium. This RF leakage, however, is extremely weak and buried under noise and other transmitted signals that can be 3-5 orders of magnitude larger. Hence, it is missed by today's radios. We design and build Ghostbuster, the first device that can reliably extract this leakage, even when it is buried under ongoing transmissions, in order to detect the hidden presence of eavesdroppers. Ghostbuster does not require any modifications to current transmitters and receivers and can accurately detect the eavesdropper in the presence of ongoing transmissions. Empirical results show that Ghostbuster can detect eavesdroppers with more than 95% accuracy up to 5 meters away.},
booktitle = {Proceedings of the 24th Annual International Conference on Mobile Computing and Networking},
pages = {337–351},
numpages = {15},
keywords = {wireless, security, eavesdropper},
location = {New Delhi, India},
series = {MobiCom '18}
}